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- Cappadona, Salvatore, et al.
(författare)
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Improved label-free LC-MS analysis by wavelet-based noise rejection
- 2010
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Ingår i: Journal of Biomedicine and Biotechnology. - : Hindawi Limited. - 1110-7251 .- 1110-7243. ; 2010
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Tidskriftsartikel (refereegranskat)abstract
- Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification.
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| 2. |
- Cappadona, Salvatore, et al.
(författare)
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Wavelet-based method for noise characterization and rejection in high-performance liquid chromatography coupled to mass spectrometry
- 2008
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Ingår i: Analytical Chemistry. - : American Chemical Society (ACS). - 1520-6882 .- 0003-2700. ; 80:13, s. 4960-4968
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Tidskriftsartikel (refereegranskat)abstract
- We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.
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